by Dr Francesca Properzi PhD
We are delighted to use this week’s blog to introduce the Centre’s new Research Manager, Francesca Properzi, who joined the team last week. Here is Francesca’s take on the way emerging technologies can improve pharma R&D.
R&D management in the pharmaceutical industry is facing unprecedented challenges. While the availability of new high-impact technologies has the potential to help reshape research and clinical development, our latest report from our Measuring the return from pharmaceutical innovation series, A new future for R&D?, shows the costs of drug development for 12 large cap pharma companies has increased sharply over the past eight years by nearly 50 per cent, totalling just under $2 billion per drug. Although a number of therapies are still expected to achieve blockbuster status, lower projected R&D returns are decreasing sharply from a once double digit high to an average of slightly over three per cent. This exceedingly low internal rate of return (IRR), is mostly due to companies paying the price of lower post-approval sales and increased R&D costs.
Could the industry’s adoption of novel technologies lead to improved IRR? How might companies readjust their strategies behind the drug development processes?
My experience working in the industry in neurodegenerative diseases and drug screening suggests there are several key themes being shaped by new emerging technologies, which pharma professionals should consider factoring into their R&D strategies if they are to be successful:
Big Data - The digital era has resulted in an unusually large data set from heterogeneous sources. This undoubtedly represents a key and valuable asset to all industries as it allows effective predictive analysis and the identification of patterns that are not possible by using small data sets and standard analytical methodologies. The applications of big data to the healthcare and pharma industry are vast. In the drug development process, big data applicability spans from high-throughput screening output, drug-repurposing, to real-world evidence. Big data management issues such as noise accumulation, storage and computational costs are being progressively minimised, and we are now entering a time when return on investment of big data application is finally delivering consistent targets.
AI and smart automation - How can AI be effectively integrated in the drug development process to reduce costs? AI algorithms applicable to big data analysis and machine learning enable accurate molecular dynamics predictions, resulting in more effective drug screening and identification, including orphan, and previously used compounds, and the creation of personalised therapeutic targets. Importantly for clinical trials, AI and machine learning standard procedures could be integrated to track patient recruitment and adherence, and understand experience patterns during treatment to help improve trial protocols. Smart automation systems are also applicable to drug screening and preclinical stages to reduce time and costs by a tighter control of inventory and materials needed, and increased reproducibility.
Innovative preclinical models – The ability to mimic human biology has been an historical hurdle for drug development preclinical testing. In contrast to preclinical data, more than half of all drugs fail during clinical trials due to a lack of efficacy,1 and about another third of drugs fail due to an insufficient therapeutic index.2 Traditionally, in vivo testing has been used as a gold standard. Nevertheless, these are long and costly experiments, which, inherently, lack accuracy and resemblance to human physiology. Novel translational approaches can provide a better understanding of the cellular and molecular mechanism associated with the drugs, reducing the risk of failure and the overall costs. Patient-derived primary cell systems, induced pluripotent stem cells and novel stem cells, 3D cell culture, are significant achievements in life sciences and invaluable resources for accurate and ethical preclinical assays. Along with recent nanotechnology developments that enable delivery of drugs selectively to target cells, they represent one of the most promising tools for novel therapy testing.
Outsourcing - From research and preclinical phases to clinical trials, external expertise could potentially add value at a lower cost. Return on investment of the R&D sector could benefit from providing venture capital investments to small and medium-sized enterprises (SMEs) in relevant medical areas and in providing project funds to specialized research centres in exchange for shared intellectual property.
Diversification - Collaboration with SMEs and research centres is also an opportunity for pharma to investigate drugs involved in more than one cellular pathway in a more effective way. This type of diversification is key for reducing the risk of failure at later stages in the drug development cycle, especially for areas where alternatives to mainstream research are uncommon and therapies are still elusive, such as rare and neurodegenerative diseases.
Drug triage - What types of drugs should be prioritized for preclinical testing? New molecular entities are now increasingly safer and supported by better data and predictive tools. There is no indication that the rate of failure at the clinical stage is higher for novel compounds compared to known drugs both in terms of efficacy and safety. Nevertheless, the cost of pharmacokinetic and dosage experiments during the preclinical stages are key considerations and could be reduced by applying a weighted drug-repurposing strategy.
Partnerships - Last year the FDA published a study, which supported the importance of collaborative clinical trials between stakeholders.3 Collaboration would allow effective testing of much needed combinational therapies and, with more than 20 per cent of trials failing due to insufficient recruitment, reduce the risk of drug development failure at later stages. Blockchain technology could also help facilitate stakeholder partnerships by allowing transparent, safe and traceable data sharing.
Finally, there is a need to develop a governance approach that reflects new and emerging regulations, guidelines and standards for the use of emerging technologies. This would facilitate innovation and speed up the implementation of novel R&D strategies. While the tools for success are already available, unravelling the synergy between them should ultimately lead to improvements in R&D and exciting times for the pharma industry, access to more targeted therapies for patients and overall benefit global health.